The Development and Validation of the Technology-Supported Reflection Inventory

Abstract

Refection is an often addressed design goal in Human-Computer Interaction (HCI) research. An increasing number of artefacts for refection have been developed in recent years. However, evaluating if and how an interactive technology helps a user refect is still complex. This makes it difcult to compare artefacts (or prototypes) for refection, impeding future design eforts. To address this issue, we developed the Technology-Supported Refection Inventory (TSRI), which is a scale that evaluates how efectively a system supports refection. We frst created a list of possible scale items based on past work in defning refection. The items were then reviewed by experts. Next, we performed exploratory factor analysis to reduce the scale to its fnal length of nine items. Subsequently, we confrmed test-retest validity of our instrument, as well as its construct validity. The TSRI enables researchers and practitioners to compare prototypes designed to support refection. CCS CONCEPTS• Human-centered computing → Empirical studies in ubiquitous and mobile computing; HCI design and evaluation methods.

A Model of Socially Sustained Self-Tracking for Food and Diet

“…We focus on describing people's learning via observation and action rather than reflection that has been less surfaced in the literature. Further work unpacking how reflection does or does not relate to different learning behaviors, building on recent work on data-mediated reflection [8,9,30] can further extend our understanding of the role of self-tracking towards learning. In presenting our model, we instead focus on describing how people's learning behaviors shape their use of both tracking technologies and social platforms, highlighting how people leverage internal and external social platforms, such as using social platforms while tracking to learn domain knowledge and practices for reaching eating goals, and the evolving from tracking to using dedicated social platforms to sustain behavior change and to obtain more specific domain knowledge.…”

Gapeau: Enhancing the Sense of Distance to Others with a Head-Mounted Sensor

“…Moreover, we recognize that the location of our study (EU country) does impact the overall discourse over pandemic restrictions and affects the social acceptability, which could be different for other areas. Future studies could potentially examine how reflection on such technologies affect perception and behaviour towards safety restrictions [8,9]. Finally, we note that our experiment used a distance higher than the safe distance as a simulation of an unsafe distance.…”

BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations

“…There is no consensus about sample size for factor analysis but general recommendations say that the more items to test, the more participants are required. In line with two suggestions [8,13] we targeted a sample size of 200 participants per visualization. We recruited participants through Prolific, who had to be fluent English speakers and to be of legal age (18 years in most countries).…”